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Artemether-lumefantrine is the most widely used artemisinin-based combination therapy for malaria. The present work aims to develop and validate a simple, accurate, precise and rapid ratio first order derivative spectrophotometric method for the simultaneous estimation of artemether and lumefantrine in a fixed dose combination tablet. The first step in development of the method was to derivatize artemether. As artemether does not show absorption in the UV region, it was derivatized using hydrochloric acid as the derivatizing agent. The derivatizing conditions were further optimized by full factorial multivariate approach, where the independent variables were volume of concentrated hydrochloric acid and time taken for artemether derivatization at room temperature. Furthermore, based on the statistical analysis, derivatizing conditions were optimized i.e. 1.3 ml of conc. HCl at room temperature for 30 min. At this condition, the artemether was found to absorb in the UV region satisfactorily, and the absorbance of lumefantrine was found to remain unaffected. The developed method showed good calibration data in the range of 5-30 g/ml for artemether and 2-12 g/ml for lumefantrine. The mean % recovery values were found to be 99.96-100.49% and 99.48-100.31% for artemether and lumefantrine, respectively. Additionally, the developed method was effectively applied in the estimation of artemether and lumefantrine in a commercial tablet (ARH-L DS tablets), suggesting that it can be practically applied for quality control of routinely examined drugs in combined dosage forms with the reduced expenditure of time.

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Coffee is one of the world’s most popular beverages, with the global coffee capsule market worth over USD 4 billion and growing. The incidence of coffee fraud is estimated to be up to one in five coffees being contaminated with cheaper blends of coffee. Given the worsening extent of climate change, coffee crop yields are harder to maintain, while demand is increasing. The 2021 Brazil frost delaying or destroying many coffee crops is an example. Hence, the incidence of coffee fraud is expected to increase, and as the market becomes more complex, there needs to be faster, easier, and more robust means of real-time coffee authentication. In this study, we propose the use of novel approaches to postcolumn derivatization (termed herein as in-column derivatization) to visualize the antioxidant profiles of coffee samples, to be later used as indicators for authentication purposes. We propose three simple mathematical similarity metrics for the real-time identification of unknown coffee samples from a sample library. Using the CUPRAC assay, and these metrics, we demonstrate the capabilities of the technique to identify unknown coffee samples from within our library of thirty.